Explainable AI (XAI) techniques aim to provide insights into predictive models and enhance user performance, yet they often fall short of these expectations. Conversational XAI assistants promise to overcome such limitations, but empirical evidence on their impact on objective performance measures remains limited. We propose an experimental design for evaluating explanation assistance through prediction accuracy, model understanding, and error identification. Using an explainable-by-design prediction model, we create conditions where users can outperform the model by identifying and compensating for systematic errors. We compare conversational assistance against Q&A-based assistance to assess which better supports users in working with model explanations. Preliminary results from testing our experimental design show that participants (N=42) in both treatments significantly outperformed the model but reveal no performance differences between assistance types and modest engagement overall. These findings inform refinements for our planned full study, including enhanced engagement interventions and investigation of the mechanisms driving improved predictions.
PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
University of Catania, Department of Economics and Business（卡塔尼亚大学经济与商业系）；Poznan University of Technology, Institute of Computing Science（波兹南技术大学计算科学研究所）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）
Explaining Any ML Model? -- On Goals and Capabilities of XAI
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Institute for Program Structures and Data Organization（程序结构与数据组织研究所）；Institute for Technology Assessment and Systems Analysis（技术评估与系统分析研究所）
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research
LUT University（拉彭兰塔-拉赫蒂工业大学）；Norwegian University of Science and Technology（挪威科技大学）；SINTEF Digital（挪威科技工业研究院数字研究所）
系统性文献综述（SLR）的最终检索查询于2022年7月15日进行。最初，我们从Scopus和Web of Science数据库中提取了1707篇期刊和会议文章。然后应用纳入和排除标准，选择58篇文章进行SLR。研究结果显示了塑造AI解释的四个维度，即格式（解释表示格式），完整性（解释应包含所有必要的信息，包括补充信息），准确性（关于解释准确性的信息）和货币（解释应包含最近的信息）。此外，随着解释的自动表示，用户可以在需要时请求附加信息。我们还发现了XAI效应的五个维度：信任、透明度、可理解性、可用性和公平性。此外，我们调查了当前的知识，从选定的文章问题化未来的研究议程，随着可能的研究路径的研究问题。因此，XAI及其对用户行为可能产生的影响的综合框架已经开发出来。
Towards a Novel Measure of User Trust in XAI Systems
UGiVIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science（巴利阿里岛屿大学数学与计算机科学系UGiVIA研究组）；Hospital Universitari Son Espases（Son Espases大学医院）；SCOPIA Research Group, Department of Mathematical Sciences and Computer Science, University of the Balearic Islands（巴利阿里岛屿大学数学与计算机科学系SCOPIA研究组）；Computer Science, University of the Balearic Islands（巴利阿里岛屿大学计算机科学系）；Institute for Health Research of the Balearic Islands (IdISBa)（巴利阿里岛屿健康研究所）；Artificial Intelligence Research Institute of the Balearic Islands (IAIB), University of the Balearic Islands（巴利阿里岛屿人工智能研究所（IAIB），巴利阿里岛屿大学）
